Implementation of a Rotational Ultrasound Biomicroscopy System Equipped with a High-Frequency Angled Needle Transducer — Ex Vivo Ultrasound Imaging of Porcine Ocular Posterior Tissues
Bibliographic record
Abstract
The mechanical scanning of a single element transducer has been mostly utilized for high-frequency ultrasound imaging. However, it requires space for the mechanical motion of the transducer. In this paper, a rotational scanning ultrasound biomicroscopy (UBM) system equipped with a high-frequency angled needle transducer is designed and implemented in order to minimize the space required. It was applied to ex vivo ultrasound imaging of porcine posterior ocular tissues through a minimal incision hole of 1 mm in diameter. The retina and sclera for the one eye were visualized in the relative rotating angle range of 270°~330° and at a distance range of 6~7 mm, whereas the tissues of the other eye were observed in relative angle range of 160°~220° and at a distance range of 7.5~9 mm. The layer between retina and sclera seemed to be bent because the distance between the transducer tip and the layer was varied while the transducer was rotated. Certin features of the rotation system such as the optimal scanning angle, step angle and data length need to be improved for ensure higher accuracy and precision. Moreover, the focal length should be considered for the image quality. This implementation represents the first report of a rotational scanning UBM system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".